Figure REF  highlights the crux of the class-imbalance problem at the support set level. Specifically, the figure shows standard meta-trained FSL models [1]} and pre-trained baselines, evaluated on the balanced 5-shot 5-way task and three imbalanced tasks. We observe that introducing even a small level of imbalance (linear 4-6-shot 5-way, \(\rho =1.5\) ) produces a significantNon-overlapping 95% confidence intervals indicate `significant' performance difference. performance difference for 6 out of 13 algorithms, compared with the balanced 5-shot task. The average accuracy drop is \(-1.5\%\)  for metric-based models and \(-8.2\%\)  for optimization-based models. On tasks with a larger imbalance (1-9shot random, \(\rho =9.0\) ), the performance drops by an average \(-8.4\%\)  for metric-based models and \(-18.0\%\)  for optimization-based models compared to the balanced task. Interestingly, despite the additional 12 samples in the support set in 1-9shot step tasks with 1 minority class (\(\rho =9.0\) ), the performance drops by \(-5.0\%\)  on the balanced task with 25 support samples in total.
<FIGURE>